Neurofeedback (NF) is a nonpharmacological approach for ADHD, but standard protocols often overlook individual EEG variability, potentially limiting efficacy. We report the first double-blind randomized controlled trial of a session-adaptive individualized upper-alpha NF protocol in children with ADHD, based on individualized 1/f + alpha model. Forty-eight children (6-12 years) diagnosed with ADHD were randomized to individualized upper-alpha NF (n = 26) or sham feedback (n = 22). Participants completed eight 1-h sessions over four weeks. The primary outcome was parent-rated ADHD symptoms on the SWAN scale, measured at baseline, post-treatment, and follow-up. Secondary outcomes included PedsQL, CBCL, SDQ, and neuropsychological tests. Intention-to-treat linear mixed-effects models assessed Time × Condition interactions; within-group changes were evaluated via paired t-tests. EEG learning was defined as the change in each session's upper-alpha/theta-to-lower-alpha ratio. Blinding was preserved (Bang's Index: caregivers = 0.13; staff = 0.21). In the NF group, 21/26 children (80.7%) achieved 'learner' status versus 36.4% in sham. Both groups showed significant reductions in ADHD symptoms post-treatment (SWAN Total: NF, d = -0.879, p < .001; sham, d = -1.261, p < .001), with no between-group difference (d = -0.062, p = .881). At six months, NF maintained improvements in ADHD symptoms (SWAN Total: d = -0.713, p = .006), without superiority over sham (d = 0.221, p = .633). Neuropsychological testing revealed NF-specific gains in Purdue Pegboard non-preferred hand (d = 1.093, FDR q = .042) and faster CPT-3 reaction time (d = -1.210, FDR q = .025). Personalized upper-alpha NF produced higher EEG learning and selective motor-speed improvements but did not outperform sham in reducing parent-rated ADHD symptoms. Eight sessions sufficed for robust EEG modulation, yet non-specific training effects likely drove clinical gains, indicating NF alone may not be an adequate ADHD treatment.

Inflammatory bowel diseases (IBD), including Crohn's disease and ulcerative colitis, are chronic conditions associated with gastrointestinal inflammation and a wide range of extraintestinal manifestations. While conventional treatments primarily address intestinal inflammation, many patients continue to experience chronic pain, psychological comorbidities, and functional gastrointestinal symptoms, highlighting the need for integrated therapeutic strategies. This narrative review explores the potential of neurofeedback (NFB) as a non-invasive therapeutic approach for managing symptoms frequently observed in IBD, including musculoskeletal pain, anxiety, depression, post-traumatic stress symptoms (PTSS), and gut-brain axis dysfunction. In the absence of direct evidence on neurofeedback in IBD populations, we examined data from clinical trials conducted in conditions that are frequently comorbid with or symptomatically overlap with inflammatory bowel disease, including fibromyalgia, irritable bowel syndrome (IBS), chronic pain syndromes, and affective or trauma-related disorders. We focused on outcomes related to chronic pain, psychological well-being, and gastrointestinal function, examining NFB protocols primarily targeting alpha, theta, and sensorimotor rhythms, which have shown promising results in improving symptom severity and autonomic regulation. These findings support the rationale for investigating NFB as a potential adjunctive therapy in IBD, particularly for patients with residual or refractory extraintestinal symptoms. However, as none of the available studies specifically involve patients with IBD, the evidence base remains indirect. Moreover, current findings are limited by small sample sizes, heterogeneous methodologies, and variable outcome measures in the studied populations, making it difficult to draw definitive conclusions regarding the efficacy of NFB in this context. Future research should include large-scale, randomized controlled trials conducted directly in IBD cohorts, along with protocol standardization and individualized approaches based on neurophysiological profiles. Clarifying the therapeutic potential of NFB in this population could open new avenues for non-pharmacological, patient-tailored interventions within integrative care frameworks.

Electroencephalography (EEG)-based neuro feedback training (NFT) guides users to regulate their neural activity via sensory feedback and further modulates their cognitive state or function. It is a crucial approach to improving brain-computer interface (BCI) performance. However, previous studies rarely report cross-task transfer effects of existing NFT approaches beyond the training task, suggesting that they may fail to improve task-related common functions. Here, we propose a steady-state visual evoked potential (SSVEP)-based table hockey BCI game as an NFT approach to improve SSVEP-BCI performance. 40 healthy subjects were randomized into four groups: the 10-frequency NFT group, the 5-frequency NFT and transfer group, the placebo group, and the blank control group. All completed a 10-frequency online SSVEP task before and after five days of training, with EEG and subjective experiences recorded throughout. The two NFT groups achieved significant improvement in online SSVEP classification accuracy. This was accompanied by increased SSVEP power, inter-trial phase coherence (ITPC), and expansion of activated cortical areas. Notably, enhancements generalized to untrained transfer tasks, likely due to the common impulse response between the training and the transfer tasks. These findings demonstrate that the proposed NFT approach not only improves SSVEP-BCI performance on the trained conditions but also induces transferable neural changes to adjacent frequencies, suggesting training augments the neural population engaged in processing steady-state visual stimuli. This work advances understanding of how self-regulation during NFT improves task-related common functions.

ObjectiveThis study evaluated whether quantitative electroencephalography (QEEG) guided neurofeedback (NF) improves intellectual functioning and attention in children with mild to severe intellectual disability (ID).MethodsSixty-five children (38 males, 27 females; mean age ≈11 years) with DSM-5 diagnoses of mild-to-severe ID (IQ 28-68) and histories of unsuccessful standard treatments were enrolled. Baseline assessments included 19-channel QEEG referenced to FDA-approved Nx-Link™, BrainDx™, and NeuroGuide™ databases, Wechsler Intelligence Scale for Children (WISC-R or WISC-IV),and the Test of Variables of Attention (TOVA). Participants underwent 80-120 QEEG-guided NF sessions. Training protocols targeted deviant z-scores, with hypercoherence addressed prior to amplitude abnormalities. IQ and attention were reassessed 6-12 months post-treatment. Outcomes were analyzed using paired t-tests, correlation analyses, and generalized linear models.ResultsMean full-scale IQ increased by approximately 10 points (≈53 to ≈63; +18.6%, p < 0.001). Significant gains were observed in both WISC-IV (+≈11 points) and WISC-R (+≈10 points), with improvements across verbal, perceptual, working memory, processing speed, and performance domains (all p < 0.001). Two thirds of participants' IQ increased by ≥6 points. TOVA scores improved but were not correlated with IQ changes. QEEG analyses showed normalization of resting-state networks, including increased alpha power in default mode network hubs and reduced coherence and phase lag; reductions in slow-wave coherence predicted IQ improvement.ConclusionQEEG-guided NF was associated with clinically meaningful improvements in intellectual functioning and attention in children with ID, exceeding gains expected from environmental enrichment. Findings support NF as a promising nonpharmacologic intervention warranting randomized controlled trials.

Neurofeedback (NF) has been proposed as a tool to support and expand access to meditation practice. Despite growing interest, no comprehensive review has examined neurofeedback for meditation-based interventions (NF-MED) across the full spectrum of practices. Here, we conducted a scoping systematic review according to PRISMA guidelines, mapping the NF-MED field across both clinical and non-clinical contexts, with three aims: (1) assess the current state of NF-MED studies, (2) evaluate how NF-MED meditative constructs and whether neural modulation shows evidence of transfer to behavioral or phenomenological outcomes, and (3) provide methodological recommendations for future work. Our analysis shows that the field is dominated by proof-of-concept studies and marked by substantial heterogeneity in design, implementation, and outcome measures, which hampers the assessment of efficacy and applicability of NF-MED. While NF-MED has been shown to consistently modulate neural activity, evidence for corresponding improvements in behavior, phenomenology, or transferable meditative skills remains limited. Additional research is thus essential for determining whether NF-MED can help practitioners overcome common meditative barriers, such as anxiety and self-doubt, and whether NF-MED can accelerate meditative development across the training spectrum from novice to advanced meditators.

Objective.Recent advances in functional magnetic resonance imaging (fMRI) have identified brain functions associated with psychiatric disorders using machine-learning classifiers that serve as potential fMRI-derived biomarkers (fMRI-BMs). Resting-state fMRI functional connectivity (FC) is gaining attention as a potential target for neurofeedback (NF) therapy. However, using electroencephalography (EEG) instead of fMRI remains an important research direction for clinical implementation. Here, we propose a novel approach for estimating an fMRI-BM from EEG signals using dynamic FC (dFC) to enable biomarker-informed EEG-based neurofeedback.Approach.We targeted a schizophrenia (SCZ) fMRI-BM defined as a weighted linear summation (WLS) of 16 FCs, previously developed as a classifier distinguishing patients with SCZ and healthy control participants (HC). We first used a resting state-fMRI dataset including 100 SCZ patients and 554 HC to examine the correlation between the original SCZ-BM and a dynamic BM computed using dFC. For the estimation, we additionally used newly acquired simultaneous EEG-fMRI data from 54 control participants. The dFC time window was set to approximately 30-60 s to support integration into an EEG-NF system. Sparse logistic regression (SLR) was applied to address overfitting caused by the high dimensionality of input features. Prediction accuracy was assessed using the area under the receiver operating characteristic curve (AUC) with leave-one-run-out cross-validation and individual-level permutation testing.Main results.The proportion of biomarker-defined states derived from dFC was strongly correlated with the original SCZ-BM across both patients with SCZ and HC (Spearman's rho = 0.77 - 0.86). Permutation testing showed significant AUC values in 19 out of 21 participants, and specificity against MDD-BM was confirmed.Significance.These findings demonstrate a methodological proof-of-concept for estimating fMRI-defined connectivity biomarkers from EEG signals, paving the way for future biomarker-informed EEG-based NF applications, while clinical applicability remains to be established.

Parkinson's disease (PD) is typically treated with medication, which often leads to side effects. This study investigated a non-pharmacological approach combining motor imagery (MI) with neurofeedback (NFB) guided by functional near-infrared spectroscopy (fNIRS) to enhance supplementary motor area (SMA) activation during MI. Three groups (N = 18 each) completed four sessions: a PD-NFB group, a healthy NFB group, and a healthy control group without NFB. The NFB groups received feedback based on decreased Δ[HbR], reflecting increased brain activity. The control group performed MI without feedback. Participants in the NFB groups generally perceived NFB to be manageable. The healthy NFB group showed significantly higher SMA activation than the noNFB group. No significant differences were observed between the PD and healthy NFB groups, or between baseline and NFB-MI in PD. This first fNIRS-guided MI-NFB study in PD (German Clinical Trials Register, DRKS-ID: DRKS00022997, DOR: 2020-10-02). Shows promise for motor rehabilitation, but also highlights further research and protocol refinement.

Successful closed-loop neurofeedback alpha frequency modulation enhances the temporal dynamics of attention.

Background:Reading impairments are common in stroke-induced aphasia and limit participation in functional and leisure activities. Traditional rehabilitation strategies show limited generalization, underscoring the need for novel interventions targeting residual neural networks.Method:This feasibility randomized controlled trial (RCT) evaluated real-time functional magnetic resonance imaging (fMRI) neurofeedback (NFB) intervention for post-stroke reading deficits. Subacute left-hemisphere stroke survivors and healthy controls (HCs) completed three weekly fMRI NFB and 10 out-of-scanner practice sessions. Stroke participants were randomized to contingent NFB (based on left supramarginal gyrus [SMG] activity, N=4) or noncontingent NFB (shuffled feedback from another participant, N=3). HCs (N=4) received contingent NFB and served as a normative reference. Primary outcomes were changes from baseline to post-intervention (~3 weeks) in task-based brain activity (motor imagery/word/nonword reading > baseline), resting state connectivity, and reading aloud. Reading comprehension was a secondary outcome. Group × session effects were tested using repeated-measures analyses and planned contrasts.Results:Task fMRI revealed training-related activation increases in left SMG (z=4.7, cluster-corrected p=0.05) and broader reading network in the contingent NFB group, particularly during nonword reading. Activation increases in noncontingent stroke group and HCs were more widespread and less reading-specific. Resting-state fMRI revealed greater integration among motor, auditory, and language networks in the contingent groups, with more disorganized patterns in the noncontingent group (permutation p=0.01; Δr=−0.1−0.1). No changes were observed in reading aloud.A significant group × session interaction was found for Reading Comprehension Battery for Aphasia, 2nd ed. (RCBA-2), (F(2,8)=8.00, p<0.05, η2=0.67). The contingent NFB stroke group improved more than HCs, (MD in RCBA-2 change =9.75, 95% CI=1.99–17.51, t(6)=3.07, p<0.05) and noncontingent NFB stroke group (RCBA-2 change=11.42, 95% CI=1.12–21.71, t(5)=2.85, p<0.05).Conclusions:These findings support the feasibility of targeting residual reading network during early recovery using fMRI NFB. Confirmation of these preliminary effects awaits completion of the ongoing RCT.Registration:ClinicalTrials.gov, NCT# NCT04875936

Efficacy of brain-based neurofeedback interventions for binge eating: a systematic review and meta-analysis of randomized controlled trials.

Pioneer studies suggested the effectiveness of food-specific electroencephalography (EEG) and real-time functional near-infrared spectroscopy (rtfNIRS) neurofeedback (NF) trainings in the treatment of binge-eating disorder (BED). These trainings aim to improve participants' neurophysiological self-regulation. However, pretreatment neurophysiological activity, a supposed key predictor of NF outcomes, remains unexplored. This preregistered analysis (https://osf.io/xsrj3) used data from a randomized-controlled trial (DRKS00014752) on 47 adults (47 ± 13 years, 81% women) with interview-assessed BED having undergone food-specific EEG- or rtfNIRS-NF (12 sessions, over 8 weeks). Bayesian linear models explored pretreatment fronto-central high beta power (23-28 Hz; indicating increased attention) in EEG, pretreatment prefrontal oxygenation in fNIRS (indicating increased cognitive control), and rapid response (RR; early reductions in binge eating) as predictors of treatment outcomes reflecting mental and physical health at posttreatment and 6-month follow-up. Higher high beta power during passive viewing of food pictures, lower high beta power, and less oxygenation during regulatory NF tasks, as well as RR predicted more favorable primary outcomes, including reduced objective binge-eating episodes and increased abstinence from binge eating. Overall, neurophysiological predictors-especially EEG activity-showed greater predictive value than RR. The preliminary findings suggest the relevance of neurophysiological activity in the prediction of NF treatment outcomes in BED. While patients with increased involuntary attention in response to food stimuli profited most from EEG-NF, those with greater difficulties in voluntary recruitment of food-related cognitive control profited most from rtfNIRS-NF. The predictors identified could guide future treatment allocation and represent a first step toward more individualized therapy approaches.

This study presents the first implementation of a neurofeedback (NF) protocol based on cordance targeting mood and anxiety disorders. Cordance, a multivariate measure of brain activity, integrates both power within frequency bands and interfrequency relationships, providing a unique perspective on neural synchronization and connectivity. Using a single-case design, a 44-year-old male patient with anxiety, depression, and insomnia was selected based on left frontal discordance. Seven NF sessions were conducted, reinforcing increases in cordance in the left anterior quadrant. The results showed significant improvements in psychometric measures, including reductions in depression, anxiety, and insomnia scores, alongside a marked shift in cordance values toward normative levels. This study introduces cordance-based NF as a potential tool for mood and anxiety regulation, offering promising preliminary evidence for its efficacy. Future research should explore larger sample sizes and longer follow-ups to confirm these findings and expand the clinical applications of cordance-based interventions.

The optimal frequency of EEG biofeedback sessions for elite athletes remains unclear, despite growing adoption of neurofeedback in high-performance sport. This randomized, controlled study compared three EEG biofeedback protocols (daily, every-other-day, every-third-day) in 24 national-level male judo athletes stratified into three phenotypic groups. Each protocol comprised 15 standardized sessions. Pre- and post-intervention assessments included functional indices (strength, power) and neurophysiological measures (Frontal Alpha Index, EMG amplitude/RMS, corrected strength sum). Biosensor performance was validated via signal quality metrics. Daily EEG biofeedback produced superior improvements in strength, FAI, and fatigue resistance. Although LRG showed the largest pre-post RMS increase (+17.44 μV vs. +16.54 μV in HRG), HRG maintained the highest post-intervention RMS values and best fatigue resistance (MF_drop = -2.15 Hz). Significant group × time interactions were observed for FAI (p = 0.027) and RMS (p = 0.019). Every-other-day protocols yielded moderate gains, while every-third-day protocols produced minimal or maladaptive EMG-load dynamics. A robust dose-response relationship was evident. Session frequency is critical for optimizing neurofeedback interventions in elite athletes. Daily EEG biofeedback confers superior adaptation compared to less frequent dosing.

Introduction: This study evaluated the effectiveness of a stepped-care approach – a staged model that escalates from lower- to higher-intensity treatments according to clinical response – for the treatment of adolescents and adults with ADHD, taking into account symptom severity and prior treatment response. Methods: In a multicenter study, adolescents and adults with ADHD (16–45 years) participated in a two-step treatment program including randomized controlled trials (RCTs). In step 1, patients were (block-)randomized to 3 months of psychoeducation (PE), telephone-assisted self-help (TASH), or waiting control (WC). In step 2 based on step 1 response (full, partial, none), patients either received counseling or were randomized to counseling with/without neurofeedback (NF) or pharmacological treatment (with/without NF) for 6 months. The primary outcome was change in clinician-rated ADHD symptoms, analyzed using linear (mixed-)effects models for repeated measures to account for correlations within participants over time. Results: Between January 2015, and September 2020, N = 299 (mean age = 28 years, 55.2% male) patients were randomized in step 1. The primary outcome showed no significant between-group differences (PE vs. TASH: d = −0.12, 95% CI: −3.18, 1.19, p = 0.64; PE vs. control: d = −0.26, 95% CI: −4.25, 0.05, p = 0.13; TASH vs. control: d = −0.14, 95% CI: −3.24, 1.04, p = 0.57). However, significant within-group effects emerged (PE: d = −0.60; TASH: d = −0.48; WC: d = −0.34; p < 0.001). Step 2 also showed no between-group differences but significant within-group effects (MPH: d = −0.59; MPH+NF: d = −0.76; counseling: d = −0.57/−1.35, p = 0.01/p < 0.001). Mixed models revealed symptom reduction in all step 1 responders and step 1 non-responders in step 2. Conclusions: The lack of step 1 RCT differences questions the specific effects of PE/TASH. Similar patterns emerged in step 2, but intensified treatment for step 1 non-responders improved outcomes in MPH groups. Step 1 response influenced later treatment success. Some stepped-care combinations did not further reduce symptoms, but no rebound effects occurred. The main limitations of this study are the two-step design complexity, limited information on certain (additional) psychosocial components, and the need to make assumptions about missing data. Nevertheless, findings support the feasibility and partial effectiveness of a stepped-care approach.

Neurofeedback (NF) technologies provide an interactive mode of self-regulation of the brain motor system in stroke rehabilitation. Simultaneous presentation of signals of two modalities, hemodynamic (functional MRI) and electrophysiological (EEG), in the feedback loop allows to compensate for the limitations of each neuroimaging technology and helps to reveal the cerebral mechanisms of motor recovery. Aim of the study was to investigate coordinated changes in fMRI signal and EEG sensorimotor rhythm power during leg movement imagination in fMRIEEG-NF sessions in a patient with stroke. Materials and methods . A patient with right-sided hemiparesis after a 6-week stroke was trained to imagine movements of the paretic foot in five fMRI-EEG-NF sessions. The feedback scale was 2/3 determined by the level of fMRI signal amplification from the supplementary motor area (SMA) and foot representation in the motor cortex (M1F) of the left hemisphere and 1/3 by desynchronization of EEG rhythms in the mu (8–13 Hz) and beta2 (18–26 Hz) frequency bands over the central midline region (electrode Cz according to the 10–20 system). Results and discussion . In most of the NF runs, the patient achieved desynchronization of the mu and beta2 rhythms of the EEG and an increase in the fMRI signal of the regions of interest. Activation of the left M1F was associated with desynchronization of mu rhythms, which is consistent with the literature. Suppression of both mu and beta2 bands correlated (p&lt;0.05) with activation of the premotor cortex bilaterally, the right primary motor cortex, and the anterior thalamus and anterior insula of the right hemisphere, indicating the involvement of the intact hemisphere in motor planning and control. The predominance of activation of homologous regions of the undamaged hemisphere, as well as the involvement of the nodes of the salience network and the dorsal attention network are consistent with the hypothesis of global functional reorganization of the brain after stroke; the same is evidenced by the strengthening of the intrahemispheric functional connectivity SMA–M1F bilaterally by the end of the course. Conclusions. For the first time, an analysis of the relationships between fMRI and EEG signals in sessions on the bimodal fMRI-EEG NF platform was performed in a patient with post-stroke foot paresis. It was shown that volitional control of the activity of M1F and SMA of the affected hemisphere also activates homologous regions of the opposite hemisphere and recruits nodes of cognitive networks, demonstrating associations with the power of the muand beta2band of EEG in the central leads.

Neurofeedback (NF) is a noninvasive neuromodulation technology achieved by teaching the subject the skill of selfregulation of certain parameters of their own brain activity in a feedback loop. It is believed that such mental training leads to changes in the functional architecture of global cerebral networks, involving neuroplasticity mechanisms, and therefore may have potential in stroke rehabilitation. EEG-NF has a long history and traditionally uses frequency bands of EEG rhythms associated with known behavioral functions as an adjustable parameter. The development of MRI technology has made it possible to obtain topographically accurate functional images of the brain (fMRI) in real time with the prospect of creating fMRI-NF-platforms. Online fusion of signals from two modalities (EEG and fMRI) in a feedback loop for self-regulation training (fMRI-EEG-NF) is attractive due to its therapeutic and research potential, promising a more detailed understanding of spatio-temporal dynamics in the brain after stroke, which is impossible to obtain using each modality separately. However, such a multimodal functional neuroimaging requires non-trivial hardware and computational solutions. The objective of the review was to trace the vector of development of the NF technology in relation to stroke rehabilitation in the historical and technological aspect. For this purpose, the theoretical and practical prerequisites for fusion of fMRI and EEG signals in a feedback loop are summarized and data on the effectiveness of NF methods as a scientifically based method of recovery after stroke are presented.

Double-blind, placebo-controlled sham neurofeedback (sNFB) trials are often regarded as the methodological gold standard for evaluating neurofeedback efficacy. However, this paradigm rests on a false equivalence with pharmacological models of intervention. Like genuine neurofeedback (NFB) and unlike inert placebos, sNFB provides structured sensory feedback—including both contingent and non-contingent reinforcement—that engages core learning mechanisms such as operant conditioning. This feature means that sham conditions are not behaviorally or physiologically inert but partially active, undermining their role as true controls. Moreover, the double-blind design is incompatible with a second essential mechanism of neurofeedback: voluntary self-regulation. To bypass this methodological limitation, research practices distance themselves from clinical standards, and in doing so reduce the active effects belonging to NFB. Together, this misalignment leads to systematic underestimation of neurofeedback’s specific effects and to misinterpretation of null findings. Drawing on parallels from psychotherapy and learning-based neuroscience, this paper argues for a reconceptualization of control design in neurofeedback research. Some suggestions for a revised framework are proposed to allow for the separation of specific from non-specific mechanisms without invoking an impossible inert placebo. Adopting this approach can realign the appraisal of neurofeedback efficacy with the principles appropriate to it as an interactive and adaptive intervention.

Obsessive-compulsive and related disorders (OCRDs) are characterized by obsessions, compulsive or repetitive behaviors, hoarding and saving, or recurrent body-focused repetitive behaviors. Although pharmacological, cognitive-behavioral, or behavioral therapies provide relief for many for these conditions, a substantial proportion respond insufficiently or experience relapses. Neurofeedback (NF) enables individuals to self-modulate neural activity in real time and has been explored as a non-invasive neuromodulation strategy in obsessive-compulsive disorder (OCD), the only OCRD in which NF has been empirically tested to date. Conventional electroencephalogram-based (EEG-NF) and functional magnetic resonance imaging-based (fMRI-NF) protocols have demonstrated that patients can learn to regulate oscillatory activity or region-specific blood-oxygen-level-dependent signals, with some studies reporting symptom improvements. However, heterogeneity in targets, limited personalization, and modest clinical effects constrain conclusions regarding efficacy and scalability. This narrative review synthesizes the existing empirical evidence of NF studies to-date. In addition, it highlights the potential for decoded neurofeedback (DecNef) as a next-generation NF method. Unlike conventional NF, DecNef leverages multivoxel pattern analysis to reinforce distributed neural representations, in some cases without explicit awareness or symptom exposure, and may allow greater precision and personalization of circuit-level interventions. We discuss how DecNef can address limitations of traditional NF and outline its potential translational applications across the OCRD spectrum. By integrating current empirical findings with a forward-looking precision psychiatry framework, this review offers ideas for conceptual and methodological advances for targeting dysfunctional neural systems underlying OCRDs.

Optimization of memory neurofeedback system utilizing intracranial electroencephalogram of the hippocampus.

Neurofeedback (NFB) is a non-invasive intervention for ADHD that aims to train self-regulation of brain activity. Despite decades of clinical application, evidence for its efficacy remains mixed, and the mechanisms-particularly the role of NFB learning in ADHD symptom reduction-are still not well understood. This study advances the NFB field by introducing both theoretically grounded and data-driven frameworks that conceptualize learning as a dynamic, individualized process. Data were drawn from a 25-month longitudinal randomized controlled trial of theta/beta ratio (TBR) NFB treatment in children with ADHD (N = 100; ages 7-10). Moving beyond prior work that characterized learning through binary classifications and group-level averages, we captured within- and between-session training dynamics and applied clustering methods to define distinct learner profiles, highlighting the heterogeneous nature of NFB responses. Our results demonstrate that while most participants learned to regulate TBR effectively, individual learning did not reliably predict symptom improvement. Instead, linear mixed-effects model results indicated that non-specific factors such as participant engagement and training context were more predictive of clinical outcomes. Moreover, variability in learning curve shapes-typically overlooked in standard NFB studies-suggests that NFB learning does not follow a uniform pattern, with individuals engaging through distinct mechanisms aligned with either operant-conditioning or skill-acquisition models. These insights advance theoretical understanding of NFB mechanisms and call for individualized, mechanism-informed approaches to optimize treatment outcomes and deepen clinical interpretation.